Quantitative Imaging in the Thorax
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Beschreibung
This book presents a comprehensive review of quantitative imaging, tracing its evolution from the extraction of imaging features to the creation of AI- and machine-specific algorithms.
Quantitative imaging refers to the measurement and analysis of image-derived features for assessing normality, disease severity, and temporal change. It encompasses the development, standardization, and optimization of imaging protocols, as well as advanced methods for data analysis, validation, and reporting.
While the traditional radiology report relies on qualitative, narrative descriptions that are intuitive but difficult to quantify, quantitative imaging introduces objectivity and reproducibility, enabling robust statistical modeling and integration with clinical outcomes. However, achieving accuracy requires rigorous quality assurance and careful methodological design.
With the rapid advancement of artificial intelligence and machine learning, and the growing demand for patient- and disease-specific models, this volume serves as a crucial reference for radiologists, clinicians, and data scientists seeking to bridge imaging science with computational medicine.
Each chapter, authored by leading international experts, explores the application of quantitative imaging in the thorax—from screening and diagnosis to prognosis and therapeutic response assessment. Dedicated sections on radiomics and radiogenomics further illustrate how imaging data can inform molecular and genetic understanding of disease.
An essential guide for radiologists, physicians, and data scientists, this book offers the foundational knowledge and practical insights needed to advance precision medicine through quantitative imaging.
This book offers a comprehensive review of quantitative imaging, from extraction of features to creating AI and machine specific algorithms. Quantitative imaging is the extraction of quantifiable features from images for the assessment of normality, severity, and degree of change. It includes the development, standardization, and optimization of protocols, data analysis, and display/reporting methods to allow for validation of image derived metrics. The radiology report is comprised of both qualitative and quantitative data. The former is narrative, detail oriented, easier for patients and other providers to understand, but difficult to summarize and hard to relate to outcome measures. Quantitative imaging on the other hand is objective and can be used in complex statistical calculations, but cannot really stand alone and needs stringent quality measures in order to preserve accuracy and reproducibility. With an increasing focus on AI and machine learning and the need to build patientand disease specific algorithms, this book offers a common ground for physicians, radiologists and data scientists to reference and further advance the field.
The main purpose of this book is to provide details of quantitative imaging from extraction of data from CT and MR images and incorporation into models for clinical translation and application. Individual chapters feature experts from around the world and covers all aspects of quantitative imaging in the thorax, from screening to diagnosis and management. This book also features chapters dedicated to radiomics and radiogenomics.
This is an ideal guide for radiologists, physicians, and data scientists working towards a common ground in quantitative imaging.
Explores every stage of quantitative imaging—from feature extraction to AI-driven clinical applications Brings together radiologists, physicians, and data scientists to bridge imaging science and computational medicine Contributions from leading international authorities in thoracic imaging, radiomics, and radiogenomics
Autor*in
Ritu Gill
Themen in »Quantitative Imaging in the Thorax«
quantitative imaging artificial intelligence machine learning thorax chest CT MR